I'm trying to speed up the research of features of stitching algorithm using OpenCL. I'm using the code of the example provided here: https ://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp I read online that the only thing I have to do is change Mat to Umat. I did it.
However I am not sure my code is actually using OpenCL.
- First: I'm working on an Ubuntu 16.04 Virtual Machine using Parallels Desktop on a Macbook Pro. Therefore the only device supported by OpenCL will be the CPU (no GPU). I installed the correct drivers, sdk, etc and CPU should work correctly. You can see the result of command "clinfo" in Ubuntu shell below. Working with a CPU, I do not expect performance improvement. My plan is just to work my virtual machine and than deploy the code on a real Ubuntu machine.
- Second: the code has no improvement (as expected, see above). Actually the required time seems to be the same. Is it right? I mean, I know I am working still on the CPU, but I expected to be some differences. Moreover looking at the call graph profiled with grof and gprof2dot there no differences (for ones who have never heard about gprof, it is simply a code profiler that can generate a call graph showing all calls among functions: which function calls what other function, and so on). Is it possible? OpenCV with and without OpenCL should call exactly the same function?
How can I be sure the code is actually working with OpenCL? I read online there were some bugs in features finding on OpenCL and therefore I would like to check myself. Moreover, obviously, I would like to work and edit the code, this is just the beginning.
I'm using this code to check if OpenCL is working:
void checkOpenCL() {
if (!cv::ocl::haveOpenCL())
{
cout << "OpenCL is not available..." << endl;
//return;
}
cv::ocl::Context context;
if (!context.create(cv::ocl::Device::TYPE_ALL))
{
cout << "Failed creating the context..." << endl;
//return;
}
cout << context.ndevices() << " CPU devices are detected." << endl; //This bit provides an overview of the OpenCL devices you have in your computer
for (int i = 0; i < context.ndevices(); i++)
{
cv::ocl::Device device = context.device(i);
cout << "name: " << device.name() << endl;
cout << "available: " << device.available() << endl;
cout << "imageSupport: " << device.imageSupport() << endl;
cout << "OpenCL_C_Version: " << device.OpenCL_C_Version() << endl;
cout << endl;
}
cv::ocl::Device(context.device(0)); //Here is where you change which GPU to use (e.g. 0 or 1)
}
And it prints:
1 CPU devices are detected.
name: Intel(R) Core(TM) i7-4850HQ CPU @ 2.30GHz
available: 1
imageSupport: 1
OpenCL_C_Version: OpenCL C 1.2
Running clinfo in Ubuntu shell report
Number of platforms 1
Platform Name Intel(R) OpenCL
Platform Vendor Intel(R) Corporation
Platform Version OpenCL 1.2 LINUX
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_icd cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_depth_images cl_khr_3d_image_writes cl_intel_exec_by_local_thread cl_khr_spir cl_khr_fp64
Platform Extensions function suffix INTEL
Platform Name Intel(R) OpenCL
Number of devices 1
Device Name Intel(R) Core(TM) i7-4850HQ CPU @ 2.30GHz
Device Vendor Intel(R) Corporation
Device Vendor ID 0x8086
Device Version OpenCL 1.2 (Build 25)
Driver Version 1.2.0.25
Device OpenCL C Version OpenCL C 1.2
Device Type CPU
Device Profile FULL_PROFILE
Max compute units 4
Max clock frequency 2300MHz
Device Partition (core)
Max number of sub-devices 4
Supported partition types by counts, equally, by names (Intel)
Max work item dimensions 3
Max work item sizes 8192x8192x8192
Max work group size 8192
Preferred work group size multiple 128
Preferred / native vector sizes
char 1 / 32
short 1 / 16
int 1 / 8
long 1 / 4
half 0 / 0 (n/a)
float 1 / 8
double 1 / 4 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero No
Round to infinity No
IEEE754-2008 fused multiply-add No
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Address bits 64, Little-Endian
Global memory size 6103834624 (5.685GiB)
Error Correction support No
Max memory allocation 1525958656 (1.421GiB)
Unified memory for Host and Device Yes
Minimum alignment for any data type 128 bytes
Alignment of base address 1024 bits (128 bytes)
Global Memory cache type Read/Write
Global Memory cache size 262144
Global Memory cache line 64 bytes
Image support Yes
Max number of samplers per kernel 480
Max size for 1D images from buffer 95372416 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x16384 pixels
Max 3D image size 2048x2048x2048 pixels
Max number of read image args 480
Max number of write image args 480
Local memory type Global
Local memory size 32768 (32KiB)
Max constant buffer size 131072 (128KiB)
Max number of constant args 480
Max size of kernel argument 3840 (3.75KiB)
Queue properties
Out-of-order execution Yes
Profiling Yes
Local thread execution (Intel) Yes
Prefer user sync for interop No
Profiling timer resolution 1ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels Yes
SPIR versions 1.2
printf() buffer size 1048576 (1024KiB)
Built-in kernels
Device Available Yes
Compiler Available Yes
Linker Available Yes
Device Extensions cl_khr_icd cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_depth_images cl_khr_3d_image_writes cl_intel_exec_by_local_thread cl_khr_spir cl_khr_fp64
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) No platform
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) No platform
clCreateContext(NULL, ...) [default] No platform
clCreateContext(NULL, ...) [other] Success [INTEL]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) No platform